Run multiple independant problems at the same time

I would like to run TFOCS_SCD function with multiple independent problems.
These problems have the same objective function but partially different inequality constraints.
I use TFOCS for Apache Spark, so I would expect it to run faster if I am able to run multiple problems at the same time instead of looping through the different problems.
Is it possible to specify multiple affineF and run the function to solve it for each of them independently? Any help is very appreciated.

val zeroVec = Array.fill(m.length)(0.0)
val rdd = sc.parallelize(zeroVec) DenseVector(_))
val objectiveF = new ProxShiftRPlus(objFunc) 
val affineF =  new LinopMatrixAdjoint(constMatrixRDD, concatConstraintsDense)
val dualProxF =  new ProjRPlus()
val x0 = rdd
val z0 = Vectors.zeros(concatConstraintsDense.size).toDense

val out = TFOCS_SCD.optimize(objectiveF, affineF, dualProxF, 0.000001, x0, z0, 10, 1e-1, 1, 2)

@Stephen_Becker is probably the only one around here who can answer this.

Thank you so much for your answer @Mark_L_Stone.
I hope that @Stephen_Becker will be kind to help on this.